# The Inverse Bagging Algorithm: Anomaly Detection by Inverse Bootstrap Aggregating

arXiv: https://arxiv.org/abs/1611.08256 · HTML: https://mlsanity.com/paper/1611.08256/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2016.

## Built on by (most cited first, 2 of 2)

- [Advances in Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider](https://mlsanity.com/paper/2105.07530/) (2021)
- [Localizing Global Discrepancies: Marginal Contributions and Contextual Anomaly Detection](https://mlsanity.com/paper/2608.28375/) (2026)

## Abstract (arXiv)

For data sets populated by a very well modeled process and by another process of unknown probability density function (PDF), a desired feature when manipulating the fraction of the unknown process (either for enhancing it or suppressing it) consists in avoiding to modify the kinematic distributions of the well modeled one. A bootstrap technique is used to identify sub-samples rich in the well modeled process, and classify each event according to the frequency of it being part of such sub-samples. Comparisons with general MVA algorithms will be shown, as well as a study of the asymptotic properties of the method, making use of a public domain data set that models a typical search for new physics as performed at hadronic colliders such as the Large Hadron Collider (LHC).